Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model

Abstract The nature of urban and rural accidents has been different from each other in some of the factors and even the severity of damage rate, mayhem, and death. In this research, using statistical methods and binary logistic regression model, we have addressed to analyze important parameters such as age, gender, education level, the color of the pedestrian dress, season of accident, time of accident, the speed of the vehicle colliding with pedestrians and road surface conditions at the time of accident on the way of death (at the scene of the incident or in the hospital) pedestrians who have been traumatized. After the creation of the binary logistic regression model, it was determined that only the parameters of speed and the accident time have been significant in the level less than 5%. And other parameters such as age, gender, the season of accident occurrence, the color of the pedestrian dress, road surface conditions and education level had no significant effect in terms of statistical on the incidence of mortality arising from a pedestrian accident with the motor vehicle. The results revealed that by adopting decisions related to the traffic calming, attention to passages lighting and brightness the mortality rate of a pedestrian due to the urban accidents can be reduced.

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Effects of the Agri-environmental Program Implemented in Sultan Reeds Area of Kayseri, Turkey

International Journal of Scientific Research and Management ◽

10.18535/ijsrm/v7i12.ah01 ◽

2019 ◽

Vol 7 (12) ◽

Author(s):

Ismet Boz

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Agricultural Practices ◽

Farm Size ◽

Chi Square ◽

Factors Affecting ◽

Environmental Program ◽

Chi Square Test ◽

Sustainable Agricultural Practices

This study was initiated to evaluate the effects of agri-environment program implemented in the Sultan reeds area of Kayseri province, Turkey. The specific objectives of the study were to compare the farmers who enrolled in the program with those who didn’t enroll regarding their application of different sustainable agricultural practices, and to determine factors affecting their enrolment in the program. The main comparative indicators were selected from different sustainable agricultural practices either promoted by the agri-environmental program or not promoted but considered very useful for the locality. Two stratified samples of farmers (enrolled and not enrolled) were selected based on their farm size. Chi-square tests of independence were used to compare farmers on the selected sustainable agricultural practices. Logistic regression model was used to determine factors affecting the enrolment of the agri-environment program. The findings of the chi-square test showed that enrolled farmers use grow more forage legumes, are more conscious about pesticides use and chemical applications, and they use more pressurized irrigation systems. Findings of the logistic regression model sowed that using rental land negatively, but contacts with extension personnel, and using long term loans for farming investments positively influenced the enrolment of the agri-environment program. Governmental effort must concentrate on these issues when promoting agri-environmental programs in the region.

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An examination of accident severity differences between male and female drivers, Using Logistic Regression Model

Civil Engineering Journal ◽

10.28991/cej-2015-00000003 ◽

2015 ◽

Vol 1 (1) ◽

pp. 31-36

Author(s):

Alireza Pakgohar ◽

Mojtaba Kazemi

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Accuracy Rate ◽

Model Accuracy ◽

Male And Female ◽

Accident Severity ◽

Probability Of Death

One person in every 2539 people gets killed and one in every 253 suffers injuries due to driving crashes each year in Iran. Such that driving incidents are second rank factor of death and the first rank reason for lost lifetimes in this country. 60% of total incidents which lead to deaths or injuries are actually driving incidents in Iran. That is while the same ratio is only 25% worldwide average. In this article, we report a probabilistic relationship between vehicle drivers’ gender and severity of the accidents. The model accuracy rate is more than 91%. Coefficient values show that if an crash happens and all other variables are under control, the probability of suffering injuries for a man is 1.597 times more than for a woman (1.40 – 1.79, 99% CI) in comparison with the case that the person does not get injured at all. Similarly, the probability of death for a man is 1.462 times higher than for a woman (1.13-1.79, 90% CI) again in comparison with case of no injury at all.

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283 Ordered logistic regression analyze the effect of elements in serum and seminal plasma on abnormal sperm rate in boars

Journal of Animal Science ◽

10.1093/jas/skz258.241 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 117-117

Author(s):

Yinghui wu ◽

chao Wang ◽

Jian Peng

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Seminal Plasma ◽

Logistic Regression Model ◽

Sperm Morphology ◽

Abnormal Sperm ◽

Ordered Logistic Regression ◽

Fe Content ◽

Icp Ms ◽

Influence Degree

Abstract Poor sperm morphology decrease sow litter size and the economic profitability of breeding herds. Our previous results revealed elements in serum and seminal plasma, such as copper, iron, and lead affects abnormal sperm rate (ABR) in boars. In this study, sperm morphology and elements in serum and seminal plasma of 385 boars were analyzed using CASA and ICP-MS, respectively from June to August in 2016. Multivariate ordered logistic regression model, which includes variables of boar breed, age, serum and seminal plasma elements was used to identify the influence degree of elements on ABR. The degree of ABR was classified grade 0: < 10%, grade 1: 10–20%, and grade 2: > 20%. Results showed ABR was influenced by boar breed, serum Cu and Fe, and seminal Pb contents (P < 0.0001). Yorkshire boars (OR: 0.321; CI: 0.187 to 0.551) and Landrace boars (OR: 0.224; CI: 0.135 to 0.371) had lower ABR than Duroc boars. Boars with serum Cu ≤2.0 mg/L had lower ABR than those with serum Cu ≥2.5 mg/L (OR: 0.483; CI: 0.281 to 0.830). ABR of boars with serum Fe ≤ 1.0mg/L was greater than that of boars with serum Fe 1.5 mg/L (OR: 2.213; CI: 1.188 to 4.120). In addition, boars with seminal plasma Pb 0 μg/L had lower ABR than those with seminal Pb ≥ 10 μg/L (OR: 0.362; CI: 0.174 to 0.757). In conclusion, Duroc boars had more risk of ABR compared with Yorkshire and Landrace boars. The decrease of seminal plasma Pb and serum Cu, and increased of serum Fe content can decrease ABR in boars.

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The Factors Affecting Eye Patients (Cataract) In Jordan by Using the Logistic Regression Model

Modern Applied Science ◽

10.5539/mas.v11n8p38 ◽

2017 ◽

Vol 11 (8) ◽

pp. 38

Author(s):

Adeeb Ahmed Ali AL Rahamneh ◽

Omar M. Hawamdeh

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Likelihood Ratio Test ◽

Logistic Regression Model ◽

Ratio Test ◽

Response Variable ◽

Factors Affecting ◽

Independent Variables ◽

Lemeshow Test ◽

The Right

This study aims to use the logistic regression model to classify patients as infected and without cataracts. The independent variables were used to represent the gender, the age, the pressure in the right eye, the pressure in the left eye, HbA1C, and the anemia, representative variables for the study of Cataract disease affects the eyes, based on a random sample of (116) patients. The results proved that the used logistic regression model is an efficient and representative for data that shows through (Likelihood Ratio Test) and (Hosmer and Lemeshow test), and the study proved that the value of (R Square Nagelkerke=1) this means that 100% of the change in the occurred changes in the response variable explained through the Logistic regression model.

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